To combat the issue of limited training samples, data augmentation is proven to be an effective tool for HSIC. It generates new samples from the original training samples without introducing additional labeling costs. Data augmentation approaches can be categorized into two main strategies as i) data wrapping; ii) oversampling [1]}. Data wrapping usually encodes several invariances (translational, size, viewpoint, and/or illumination) by conducting geometric and color-based transformations while preserving the labels, and oversampling-based augmentation methods inflate the training data by generating synthetic samples based on original data distributions. Oversampling techniques include mixture-based instance generation, feature space augmentations [1]}, and Generative Adversarial Networks (GANs) [3]}.
